An AI powered chat bot assistant for business owners to help them learn detailed info about their business and micro manage it based on their own database.
The challenges this chatbot would face include:
understanding the prompt in the first place for example for “what product should do we need to buy more of?”
training the model with the custom database
translating the prompt into queries
mixing the query results into optimized human-understandable answers
We need to fine-tune our model with user’s custom database and a set of special business related words.
fine-tuning an LLM involves tailoring the pre-trained model for specific tasks by adjusting parameters based on a new dataset, this process presents challenges such as high training costs, difficulty in updating information, and limited insight into the model’s decision-making process.
In contrast, the context augmentation pattern of Retrieval-Augmented Generation (RAG) presents a promising alternative. By supplementing the input context with additional information or data, RAG enhances the model’s understanding and performance, resulting in outputs that are more accurate and contextually relevant.
Then we use a text-to-sql model like T5 or BERT to generate the result but the users’s prompt could be complex and ambiguous therefore we need to add a layer to simplify and translate the prompt into something our model can process for example “what product are we selling the most?”
the returned query result might not be user-friendly so as for the last step we paraphrase it and return the result
In the bustling world of modern business, imagine a digital companion – an AI assistant with the power to unlock the secrets hidden within mountains of business data. This intelligent helper, like a wise advisor, stands ready to guide business owners through their daily challenges and decisions.
But creating such a magical assistant comes with its challenges. Picture trying to teach a foreigner your local dialect – that’s similar to our first challenge. When a business owner asks seemingly simple questions like “What products should we stock up on?”, our digital friend must first decode the true meaning behind these words.
Our hero’s journey continues with an interesting twist – it needs to become familiar with each unique business’s world. Like a student learning from a special textbook, the AI must absorb and understand the specific database of each business it serves. This is where the tale takes an innovative turn.
Instead of following the traditional path of complete retraining (which would be like rewriting an entire book), our clever assistant uses a technique called RAG – think of it as having a smart reference guide always at hand. This approach allows our AI to enhance its knowledge without having to relearn everything from scratch.
The next chapter of our story involves translation magic. Using powerful tools like T5 or BERT (think of them as universal translators), our assistant converts human questions into a language databases can understand. It’s like turning “What’s flying off our shelves?” into a precise database query.
But the final act is perhaps the most important – taking raw data and transforming it into a story that makes sense to humans. It’s like turning a complex recipe into simple cooking instructions that anyone can follow.
Through this journey, our AI assistant becomes not just a tool, but a trusted business partner, helping owners understand and manage their businesses better, one question at a time. It’s a perfect blend of artificial intelligence and practical business wisdom, working together to write success stories for businesses everywhere.
This modern tale shows us how technology can transform the way we understand and run our businesses, making complex data management feel as natural as having a conversation with a knowledgeable friend.
